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Measures in DEA with an Application to the Malmquist Index

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  • Robert Thrall

Abstract

This paper shows the importance of goal vectors G in measuring and dealing with DEA inefficiencies. It emphasizes the advantages of the family of additive relative to the traditional oriented DEA models and shifts the primary emphasis to measuring inefficiency rather than efficiency. This new (raw) inefficiency measure RIN incorporates both the traditional DEA efficiency and the DEA slacks and provides the background for a new approach to the Malmquist Index. The final section points out some deficiencies in existing computational procedures for selecting G and calls for continued research on the selection process, as well as showing a role for G in returns to scale studies. Copyright Kluwer Academic Publishers 2000

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  • Robert Thrall, 2000. "Measures in DEA with an Application to the Malmquist Index," Journal of Productivity Analysis, Springer, vol. 13(2), pages 125-137, March.
    • Handle: RePEc:kap:jproda:v:13:y:2000:i:2:p:125-137
    DOI: 10.1023/A:1007800830737
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    References listed on IDEAS

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    1. Banker, Rajiv D. & Thrall, R. M., 1992. "Estimation of returns to scale using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 62(1), pages 74-84, October.
    2. Balk, Bert M, 1993. "Malmquist Productivity Indexes and Fisher Ideal Indexes: Comment," Economic Journal, Royal Economic Society, vol. 103(418), pages 680-682, May.
    3. William Cooper & Kyung Park & Jesus Pastor, 1999. "RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA," Journal of Productivity Analysis, Springer, vol. 11(1), pages 5-42, February.
    4. Fare, Rolf & Grosskopf, Shawna, 1992. "Malmquist Productivity Indexes and Fisher Ideal Indexes," Economic Journal, Royal Economic Society, vol. 102(410), pages 158-160, January.
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    4. Martin Boďa & Mariana Považanová, 2020. "Productivity patterns in Europe: adaptation of the Malmquist index to measuring group performance and productivity change over time," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 949-989, November.
    5. Hadjicostas, Petros & Soteriou, Andreas C., 2006. "One-sided elasticities and technical efficiency in multi-output production: A theoretical framework," European Journal of Operational Research, Elsevier, vol. 168(2), pages 425-449, January.
    6. W. Cooper & Shanling Li & L. Seiford & Kaoru Tone & R. Thrall & J. Zhu, 2001. "Sensitivity and Stability Analysis in DEA: Some Recent Developments," Journal of Productivity Analysis, Springer, vol. 15(3), pages 217-246, May.
    7. Cooper, W.W. & Pastor, Jesus T. & Aparicio, Juan & Borras, Fernando, 2011. "Decomposing profit inefficiency in DEA through the weighted additive model," European Journal of Operational Research, Elsevier, vol. 212(2), pages 411-416, July.
    8. Banker, Rajiv D. & Cooper, William W. & Seiford, Lawrence M. & Thrall, Robert M. & Zhu, Joe, 2004. "Returns to scale in different DEA models," European Journal of Operational Research, Elsevier, vol. 154(2), pages 345-362, April.
    9. I Herrero & S Pascoe, 2004. "Analysing the effect of technical change on individual outputs using modified quasi-Malmquist indexes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1081-1089, October.
    10. Béchir Ben Lahouel & Younes Ben Zaied & Guo-liang Yang & Maria-Giuseppina Bruna & Yaoyao Song, 2022. "A non-parametric decomposition of the environmental performance-income relationship: evidence from a non-linear model," Annals of Operations Research, Springer, vol. 313(1), pages 525-558, June.
    11. Apostolos Christopoulos & Ioannis Dokas & Sofia Katsimardou & Eleftherios Spyromitros, 2022. "The Malmquist Productivity measure for UK-listed firms in the aftermath of the global financial crisis," Operational Research, Springer, vol. 22(2), pages 1617-1634, April.
    12. Cooper, W. W. & Deng, Honghui & Gu, Bisheng & Li, Shanling & Thrall, R. M., 2001. "Using DEA to improve the management of congestion in Chinese industries (1981-1997)," Socio-Economic Planning Sciences, Elsevier, vol. 35(4), pages 227-242, December.
    13. Adel Hatami-Marbini & Aliasghar Arabmaldar & John Otu Asu, 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1213-1254, December.
    14. Degla, Pamphile, 2015. "Technical Efficiency in Producing Cashew Nuts in Benin’s Savanna Zone, West Africa," Quarterly Journal of International Agriculture, Humboldt-Universitaat zu Berlin, vol. 54(2), pages 1-16, May.
    15. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2009. "An occurrence of multiple projections in DEA-based measurement of technical efficiency: Theoretical comparison among DEA models from desirable properties," European Journal of Operational Research, Elsevier, vol. 196(2), pages 764-794, July.
    16. Emrouznejad, Ali & Yang, Guo-liang, 2016. "CO2 emissions reduction of Chinese light manufacturing industries: A novel RAM-based global Malmquist–Luenberger productivity index," Energy Policy, Elsevier, vol. 96(C), pages 397-410.
    17. Hatami-Marbini, A. & Arabmaldar, A. & Otu Asu, J., 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 138964, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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